- A
Create a SageMaker multi-model endpoint with automatic scaling.
Why wrong: Multi-model endpoints can auto-scale, but the question does not mention multiple models.
- B
Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy.
Real-time endpoints with auto-scaling adjust instance count based on load.
- C
Use SageMaker Serverless Inference which scales automatically.
Why wrong: Serverless is a different offering, not an endpoint configuration.
- D
Use SageMaker Batch Transform with a scheduled job.
Why wrong: Batch Transform is for batch, not real-time.
Question 225 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to use Amazon SageMaker to host a model that was trained using a custom algorithm. The model artifact is stored in Amazon S3. The company wants to ensure that the endpoint can automatically scale based on the number of incoming requests. Which configuration should the company use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy.
Option B is correct because a SageMaker real-time endpoint with automatic scaling using a target tracking policy allows the endpoint to dynamically adjust the number of instances based on the incoming request load. This configuration is ideal for hosting a custom algorithm model artifact stored in S3, as it provides low-latency inference and can scale out or in based on a target metric like average CPU utilization or request count per instance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a SageMaker multi-model endpoint with automatic scaling.
Why it's wrong here
Multi-model endpoints can auto-scale, but the question does not mention multiple models.
- ✓
Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy.
Why this is correct
Real-time endpoints with auto-scaling adjust instance count based on load.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker Serverless Inference which scales automatically.
Why it's wrong here
Serverless is a different offering, not an endpoint configuration.
- ✗
Use SageMaker Batch Transform with a scheduled job.
Why it's wrong here
Batch Transform is for batch, not real-time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse SageMaker Serverless Inference with real-time endpoints, assuming serverless automatically handles all scaling needs, but they overlook the limitations of serverless (e.g., model size limits, cold starts, and concurrency caps) that make it unsuitable for many custom algorithms, especially those requiring high throughput or large artifacts.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker real-time endpoint automatic scaling uses Application Auto Scaling with a target tracking policy that monitors a CloudWatch metric (e.g., SageMakerVariantInvocationsPerInstance) and adjusts the desired instance count to maintain the target value. This is similar to how AWS Auto Scaling works for EC2, but specifically tailored for SageMaker endpoints, and it supports both simple and step scaling policies for more granular control. A real-world scenario where this matters is a production inference API that experiences unpredictable traffic spikes, such as a retail recommendation engine during a flash sale, where the endpoint must scale quickly to maintain low latency without over-provisioning.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a SageMaker real-time endpoint and configure automatic scaling using a target tracking policy. — Option B is correct because a SageMaker real-time endpoint with automatic scaling using a target tracking policy allows the endpoint to dynamically adjust the number of instances based on the incoming request load. This configuration is ideal for hosting a custom algorithm model artifact stored in S3, as it provides low-latency inference and can scale out or in based on a target metric like average CPU utilization or request count per instance.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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